Autonomous vehicle control based on receding horizon method

Abstract

This paper presents the model, simulation and experimental results of model predictive control (MPC) for motion control of an autonomous vehicle. The simulation and experiments were designed for mining application. In mining industry robotic vehicles have to navigate in tunnels and often have to overcome inclined and rough surfaces. In this case, the MPC was applied using a three-dimensional dynamic model of the robot vehicle. The purpose is to achieve autonomous vehicle motion with the avoidance of wheel-ground slippage or loss of contact while, at the same time, conserving the geometric path planning results by modifying the input commands. Tests were carried out for a vehicle moving on an inclined plane. Results from testing MPC plus feedback linearization controllers are presented and compared with results for controllers using only feedback linearization. Simulation results are presented for a three-wheeled vehicle moving in conditions that permit to illustrate the performance of the proposed controller. The results obtained show that the MPC is an efficient method that contributes to accurate control and navigation of an autonomous vehicle. The results were used to evaluate the performance and operational capabilities of an autonomous unmanned mining vehicle.